Mapping sclerobiosis: a new method for interpreting the distribution, biological implications, and paleoenvironmental significance of sclerobionts on biotic hosts
Bibliographic record
Abstract
Abstract The use of sclerobiosis as a tool for paleoenvironmental and paleoecological research is undermined by a lack of comparable methods for sclerobiont data collection and analysis. We present a new method for mapping sclerobiont distributions across any host, and offer an example of how the method may be used to interpret sclerobiont data in relation to host orientation. This approach can also be used to assess the suitability of beds and fossil material for paleoenvironmental reconstruction. A sample of 150 encrusted dorsibiconvex atrypide brachiopods were selected from six beds in the Waterways Formation (latest Givetian – Early Frasnian; Alberta, Canada). The dorsal and ventral valves of each brachiopod were photographed. Sclerobiont taxa were mapped onto the photographs, and the maps were used to create stacked images with each of the 25 brachiopod specimens from each bed. Based on the life orientation of dorsibiconvex atrypides, three zones were designated on the host: the post mortem zone, (only available to sclerobionts after death and reorientation of the host); the shaded zone (brachial valve, excluding the post mortem zone); and the exposed zone (ventral valve). Randomization simulation results indicate that all beds likely exhibit non random encrustation patterns, and corroborate the hypotheses that: (1) much of the encrustation occurred while the hosts were alive, and (2) these beds and fossils have experienced little physical reworking or transport and would be suitable for paleoenvironmental analysis. Mapping sclerobionts across hosts can serve as a unifying method to increase the recognition and use of sclerobiosis in paleontological studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".